Existing interactive point cloud segmentation approaches primarily focus on
the object segmentation, which aim to determine which points belong to the
object of interest guided by user interactions. This paper concentrates on an
unexplored yet meaningful task, i.e., interactive point cloud semantic
segmentation, which assigns high-quality semantic labels to all points in a
scene with user corrective clicks. Concretely, we presents the first
interactive framework for point cloud semantic segmentation, named InterPCSeg,
which seamlessly integrates with off-the-shelf semantic segmentation networks
without offline re-training, enabling it to run in an on-the-fly manner. To
achieve online refinement, we treat user interactions as sparse training
examples during the test-time. To address the instability caused by the sparse
supervision, we design a stabilization energy to regulate the test-time
training process. For objective and reproducible evaluation, we develop an
interaction simulation scheme tailored for the interactive point cloud semantic
segmentation task. We evaluate our framework on the S3DIS and ScanNet datasets
with off-the-shelf segmentation networks, incorporating interactions from both
the proposed interaction simulator and real users. Quantitative and qualitative
experimental results demonstrate the efficacy of our framework in refining the
semantic segmentation results with user interactions. The source code will be
publicly available.

通过与现有的语义分割网络相无缝集成且无需离线重新训练，本文提出了一种名为 InterPCSeg 的用于点云语义分割的交互式框架，该框架能够通过用户纠正点击在线进行点云的高质量语义标注，并通过设计一个稳定能量来调节测试时训练过程，以解决稀疏监督引起的不稳定性，实验证明该框架在与用户交互时能够有效地改进语义分割结果。